What is Gradient?Gradient kya hai?

🧸 Think of it likeAise samjho

Like feeling which way the ground slopes under your feet: it tells you which direction makes the error go up, so you step the other way.Pairon ke neeche zameen ka dhalaan mehsoos karne jaisa: batata hai kis taraf error badhta hai, toh aap ulti taraf kadam rakhte ho.

📘 ExplainedVistaar se

For each weight, the gradient says how much the loss would change if that weight changed slightly. Training uses gradients to update all weights at once. Backpropagation computes them efficiently.Har weight ke liye gradient batata hai ki weight thoda badalne pe loss kitna badlega. Training gradients se saare weights ek saath update karti hai. Backpropagation inhe efficiently nikaalta hai.

💡 Real exampleAsli example

If increasing a weight raises the error, its gradient is positive, so training decreases it.Agar weight badhaane se error badhe, to uska gradient positive hai, isliye training use ghataati hai.

📖 Learn it step by stepStep by step seekho

• Gradients and gradient descent: how models improveGradients aur gradient descent: models behtar kaise hote hain

🔗 Related termsJude hue shabd

• Gradient descentThe training method that repeatedly adjusts weights in small steps to reduce error.Training ka tarika jo error kam karne ke liye weights ko baar baar chhote kadmon mein badalta hai.
• BackpropagationThe method neural networks use to work out how much each weight contributed to the error, so it can be adjusted.Woh tarika jisse neural network pata lagata hai har weight ne error mein kitna yogdaan diya, taaki use badla ja sake.
• Loss functionA formula that measures how wrong the model's predictions are; training tries to make it small.Formula jo naapta hai model ki predictions kitni galat hain; training ise chhota karne ki koshish karti hai.